{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2207c56f-11b3-4f07-b815-1ab66c3d8b23",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Py3_Jupyter_Nb_Pandas(金融RSI指标分析)_GF_2024-01-22.ipynb\n",
    "# Create By GF 2024-01-22 23:26"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b2874876",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd # -> Pandas Version 1.4.1\n",
    "import matplotlib.pyplot as plt # -> Matplotlib Version 3.6.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "346e636a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Matplotlib 相关配置。\n",
    "# ##################################################\n",
    "plt.rcParams[\"font.sans-serif\"] = [\"SimHei\"]\n",
    "plt.rcParams[\"axes.unicode_minus\"] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a6e0a494",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 全局变量(Global Variable)。\n",
    "# ##################################################\n",
    "RSI_Chg_List:list = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b4345811",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Map 函数 - 金融指标(Finance Indicator) - 相对强弱指标 (Finance - Relative Strength Index).\n",
    "def MapFunc_FinInd_RSI(Index:int, Period:int, Change:float) -> float:\n",
    "\n",
    "    # 相对强弱指标 RSI 是用以计测市场供需关系和买卖力道的方法及指标。\n",
    "    # \n",
    "    # 公式一:\n",
    "    # RSI(N) = A ÷ ( A + B ) × 100\n",
    "    # A = N 日内收盘价所有上涨额度之和\n",
    "    # B = N 日内收盘价所有下跌额度之和(取正数, 即乘以(-1))\n",
    "    # \n",
    "    # 公式二:\n",
    "    # RS(相对强度) = N日内收盘价所有上涨额度之和的平均值 ÷ N日内收盘价所有下跌额度之和的平均值(取绝对值)\n",
    "    # RSI(相对强弱指标) = 100 - 100 ÷ ( 1 + RS )\n",
    "    # \n",
    "    # 这两个公式虽然有些不同, 但计算的结果一样。\n",
    "    # \n",
    "    # 股票 RSI 三条线分别为 RSI1, RSI2, RSI3。\n",
    "    # RSI1 是白线，一般指 6 日相对强弱指标;\n",
    "    # RSI2 是黄线，一般指 12 日相对强弱指标;\n",
    "    # RSI3 是紫线，一般指 24 日相对强弱指标.\n",
    "    # --------------------------------------------------\n",
    "    global RSI_Chg_List\n",
    "    \n",
    "    # --------------------------------------------------\n",
    "    if   (Index == 1):\n",
    "        \n",
    "        RSI_Chg_List.clear()\n",
    "        # ..............................................\n",
    "        RSI_Chg_List.append(Change)\n",
    "        # ..............................................\n",
    "        return None\n",
    "    \n",
    "    elif (1 < Index < Period):\n",
    "        \n",
    "        RSI_Chg_List.append(Change)\n",
    "        # ..............................................\n",
    "        return None\n",
    "    \n",
    "    else:\n",
    "        \n",
    "        # --------------------------------------------------\n",
    "        RSI_Chg_List.append(Change)\n",
    "        \n",
    "        # ----------------------------------------------\n",
    "        Idx = (Index - 1) # -> 由于行号索引是从 1 开始，但 Python 列表索引是从 0 开始, 所以需要减去 1。\n",
    "        \n",
    "        # 提取周期内上涨之和(Change Up)和周期内下跌之和(Change Down)。\n",
    "        # ----------------------------------------------\n",
    "        Chg_Up_Sum:float = 0.0 # -> 周期内的上涨之和.\n",
    "        Chg_Dn_Sum:float = 0.0 # -> 周期内的下跌之和.\n",
    "        # ..............................................\n",
    "        Chg_Up_Sum = sum([RSI_Chg_List[i] for i in range((Idx + 1 - Period), (Idx + 1)) if RSI_Chg_List[i] >= 0.0])\n",
    "        Chg_Dn_Sum = sum([RSI_Chg_List[i] * (-1) for i in range((Idx + 1 - Period), (Idx + 1)) if RSI_Chg_List[i] < 0.0])\n",
    "        \n",
    "        # 计算 RSI。\n",
    "        # ----------------------------------------------\n",
    "        # 每天既没上涨也没下跌, 最近 N 天的所有的 Up Move 之和是 0, 最近 N 天的所有的 Down Move 之和是 0, RS 会是 0 除以 0。\n",
    "        # 但实际并不处于每天都是上涨或每天都是下跌的情况, 所以行情属于中性, 这种特殊情况定义 RSI 为 50。\n",
    "        if   (Chg_Up_Sum == 0.0) and (Chg_Dn_Sum == 0.0):\n",
    "            \n",
    "            return float(50.0)\n",
    "        \n",
    "        # 每天都是下跌, 这将导致没有 Up Move 的日期, 最近 N 天的所有的 Up Move 之和是 0, Down Move 会是某个正数, 0 除以某个正数是 0。\n",
    "        # 所以这种特殊情况会定义 RSI 为 0。\n",
    "        elif (Chg_Up_Sum == 0.0) and (Chg_Dn_Sum != 0.0):\n",
    "            \n",
    "            return float(0.0)\n",
    "        \n",
    "        # 每天都是上涨, 这将导致没有 Down Move 的日期, 最近 N 天的所有的 Down Move 之和是 0, RS 会是某个正数除以 0, 数学上这是非法的。\n",
    "        # 所以这种特殊情况会定义 RSI 为 100。\n",
    "        elif (Chg_Up_Sum != 0.0) and (Chg_Dn_Sum == 0.0):\n",
    "            \n",
    "            return float(100.0)\n",
    "        \n",
    "        else:\n",
    "            \n",
    "            RS:float = (Chg_Up_Sum / Period) / (Chg_Dn_Sum / Period)\n",
    "            # ..........................................\n",
    "            RSI:float = (100 - 100 / (1 + RS))\n",
    "            # ..............................................\n",
    "            return RSI\n",
    "    # ##################################################\n",
    "    # End of Function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5ce7f534",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 读取 CSV。\n",
    "# ##################################################\n",
    "StkPDF = pd.read_csv(\"./Datas/Stock.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "308c64fd",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Code</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Pre_Close</th>\n",
       "      <th>Change</th>\n",
       "      <th>Chg_Pct</th>\n",
       "      <th>Turnover</th>\n",
       "      <th>Volume</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-12-31</td>\n",
       "      <td>'000422</td>\n",
       "      <td>7.93</td>\n",
       "      <td>7.95</td>\n",
       "      <td>7.76</td>\n",
       "      <td>7.77</td>\n",
       "      <td>7.93</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>-0.020177</td>\n",
       "      <td>0.015498</td>\n",
       "      <td>13915200</td>\n",
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       "      <td>6976420000</td>\n",
       "      <td>6976420000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-12-30</td>\n",
       "      <td>'000422</td>\n",
       "      <td>7.86</td>\n",
       "      <td>7.93</td>\n",
       "      <td>7.75</td>\n",
       "      <td>7.93</td>\n",
       "      <td>7.84</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.011480</td>\n",
       "      <td>0.018662</td>\n",
       "      <td>16755900</td>\n",
       "      <td>131567000</td>\n",
       "      <td>7120080000</td>\n",
       "      <td>7120080000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-12-29</td>\n",
       "      <td>'000422</td>\n",
       "      <td>7.72</td>\n",
       "      <td>7.85</td>\n",
       "      <td>7.69</td>\n",
       "      <td>7.84</td>\n",
       "      <td>7.71</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.016861</td>\n",
       "      <td>0.015886</td>\n",
       "      <td>14263800</td>\n",
       "      <td>110789000</td>\n",
       "      <td>7039280000</td>\n",
       "      <td>7039280000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-12-28</td>\n",
       "      <td>'000422</td>\n",
       "      <td>8.03</td>\n",
       "      <td>8.08</td>\n",
       "      <td>7.70</td>\n",
       "      <td>7.71</td>\n",
       "      <td>8.03</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>-0.039851</td>\n",
       "      <td>0.030821</td>\n",
       "      <td>27672800</td>\n",
       "      <td>218869000</td>\n",
       "      <td>6922550000</td>\n",
       "      <td>6922550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-12-25</td>\n",
       "      <td>'000422</td>\n",
       "      <td>8.03</td>\n",
       "      <td>8.05</td>\n",
       "      <td>7.93</td>\n",
       "      <td>8.03</td>\n",
       "      <td>7.99</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.005006</td>\n",
       "      <td>0.021132</td>\n",
       "      <td>18974000</td>\n",
       "      <td>151673000</td>\n",
       "      <td>7209870000</td>\n",
       "      <td>7209870000</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2647</th>\n",
       "      <td>2005-02-04</td>\n",
       "      <td>'000422</td>\n",
       "      <td>6.75</td>\n",
       "      <td>7.05</td>\n",
       "      <td>6.71</td>\n",
       "      <td>7.00</td>\n",
       "      <td>6.75</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.037037</td>\n",
       "      <td>0.017178</td>\n",
       "      <td>2430800</td>\n",
       "      <td>16818500</td>\n",
       "      <td>1725750000</td>\n",
       "      <td>990538000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2648</th>\n",
       "      <td>2005-02-03</td>\n",
       "      <td>'000422</td>\n",
       "      <td>7.00</td>\n",
       "      <td>7.15</td>\n",
       "      <td>6.73</td>\n",
       "      <td>6.75</td>\n",
       "      <td>6.90</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.021739</td>\n",
       "      <td>0.028556</td>\n",
       "      <td>4040880</td>\n",
       "      <td>28086200</td>\n",
       "      <td>1664110000</td>\n",
       "      <td>955162000</td>\n",
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       "    <tr>\n",
       "      <th>2649</th>\n",
       "      <td>2005-02-02</td>\n",
       "      <td>'000422</td>\n",
       "      <td>6.42</td>\n",
       "      <td>6.99</td>\n",
       "      <td>6.42</td>\n",
       "      <td>6.90</td>\n",
       "      <td>6.42</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.074766</td>\n",
       "      <td>0.032928</td>\n",
       "      <td>4659550</td>\n",
       "      <td>31345900</td>\n",
       "      <td>1701090000</td>\n",
       "      <td>976388000</td>\n",
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       "    <tr>\n",
       "      <th>2650</th>\n",
       "      <td>2005-02-01</td>\n",
       "      <td>'000422</td>\n",
       "      <td>6.78</td>\n",
       "      <td>6.89</td>\n",
       "      <td>6.30</td>\n",
       "      <td>6.42</td>\n",
       "      <td>6.81</td>\n",
       "      <td>-0.39</td>\n",
       "      <td>-0.057269</td>\n",
       "      <td>0.027348</td>\n",
       "      <td>3869880</td>\n",
       "      <td>25333700</td>\n",
       "      <td>1582760000</td>\n",
       "      <td>908465000</td>\n",
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       "    <tr>\n",
       "      <th>2651</th>\n",
       "      <td>2005-01-31</td>\n",
       "      <td>'000422</td>\n",
       "      <td>6.78</td>\n",
       "      <td>6.87</td>\n",
       "      <td>6.70</td>\n",
       "      <td>6.81</td>\n",
       "      <td>6.94</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>-0.018732</td>\n",
       "      <td>0.018862</td>\n",
       "      <td>2669130</td>\n",
       "      <td>18135800</td>\n",
       "      <td>1678910000</td>\n",
       "      <td>963652000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2652 rows × 14 columns</p>\n",
       "</div>"
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       "           Date     Code  Open  High   Low  Close  Pre_Close  Change  \\\n",
       "0    2015-12-31  '000422  7.93  7.95  7.76   7.77       7.93   -0.16   \n",
       "1    2015-12-30  '000422  7.86  7.93  7.75   7.93       7.84    0.09   \n",
       "2    2015-12-29  '000422  7.72  7.85  7.69   7.84       7.71    0.13   \n",
       "3    2015-12-28  '000422  8.03  8.08  7.70   7.71       8.03   -0.32   \n",
       "4    2015-12-25  '000422  8.03  8.05  7.93   8.03       7.99    0.04   \n",
       "...         ...      ...   ...   ...   ...    ...        ...     ...   \n",
       "2647 2005-02-04  '000422  6.75  7.05  6.71   7.00       6.75    0.25   \n",
       "2648 2005-02-03  '000422  7.00  7.15  6.73   6.75       6.90   -0.15   \n",
       "2649 2005-02-02  '000422  6.42  6.99  6.42   6.90       6.42    0.48   \n",
       "2650 2005-02-01  '000422  6.78  6.89  6.30   6.42       6.81   -0.39   \n",
       "2651 2005-01-31  '000422  6.78  6.87  6.70   6.81       6.94   -0.13   \n",
       "\n",
       "       Chg_Pct  Turnover    Volume     Amount       Total      Circle  \n",
       "0    -0.020177  0.015498  13915200  109318000  6976420000  6976420000  \n",
       "1     0.011480  0.018662  16755900  131567000  7120080000  7120080000  \n",
       "2     0.016861  0.015886  14263800  110789000  7039280000  7039280000  \n",
       "3    -0.039851  0.030821  27672800  218869000  6922550000  6922550000  \n",
       "4     0.005006  0.021132  18974000  151673000  7209870000  7209870000  \n",
       "...        ...       ...       ...        ...         ...         ...  \n",
       "2647  0.037037  0.017178   2430800   16818500  1725750000   990538000  \n",
       "2648 -0.021739  0.028556   4040880   28086200  1664110000   955162000  \n",
       "2649  0.074766  0.032928   4659550   31345900  1701090000   976388000  \n",
       "2650 -0.057269  0.027348   3869880   25333700  1582760000   908465000  \n",
       "2651 -0.018732  0.018862   2669130   18135800  1678910000   963652000  \n",
       "\n",
       "[2652 rows x 14 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 转换类型: 初次读取后转换。\n",
    "# ##################################################\n",
    "StkPDF[\"Date\"] = StkPDF[\"Date\"].astype(\"datetime64[ns]\")\n",
    "\n",
    "# ##################################################\n",
    "StkPDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a4945783",
   "metadata": {},
   "outputs": [
    {
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       "      <td>2015-12-31</td>\n",
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       "      <td>16755900</td>\n",
       "      <td>131567000</td>\n",
       "      <td>7120080000</td>\n",
       "      <td>7120080000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-12-29</td>\n",
       "      <td>'000422</td>\n",
       "      <td>7.72</td>\n",
       "      <td>7.85</td>\n",
       "      <td>7.69</td>\n",
       "      <td>7.84</td>\n",
       "      <td>7.71</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.016861</td>\n",
       "      <td>0.015886</td>\n",
       "      <td>14263800</td>\n",
       "      <td>110789000</td>\n",
       "      <td>7039280000</td>\n",
       "      <td>7039280000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-12-28</td>\n",
       "      <td>'000422</td>\n",
       "      <td>8.03</td>\n",
       "      <td>8.08</td>\n",
       "      <td>7.70</td>\n",
       "      <td>7.71</td>\n",
       "      <td>8.03</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>-0.039851</td>\n",
       "      <td>0.030821</td>\n",
       "      <td>27672800</td>\n",
       "      <td>218869000</td>\n",
       "      <td>6922550000</td>\n",
       "      <td>6922550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-12-25</td>\n",
       "      <td>'000422</td>\n",
       "      <td>8.03</td>\n",
       "      <td>8.05</td>\n",
       "      <td>7.93</td>\n",
       "      <td>8.03</td>\n",
       "      <td>7.99</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.005006</td>\n",
       "      <td>0.021132</td>\n",
       "      <td>18974000</td>\n",
       "      <td>151673000</td>\n",
       "      <td>7209870000</td>\n",
       "      <td>7209870000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2647</th>\n",
       "      <td>2005-02-04</td>\n",
       "      <td>'000422</td>\n",
       "      <td>6.75</td>\n",
       "      <td>7.05</td>\n",
       "      <td>6.71</td>\n",
       "      <td>7.00</td>\n",
       "      <td>6.75</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.037037</td>\n",
       "      <td>0.017178</td>\n",
       "      <td>2430800</td>\n",
       "      <td>16818500</td>\n",
       "      <td>1725750000</td>\n",
       "      <td>990538000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2648</th>\n",
       "      <td>2005-02-03</td>\n",
       "      <td>'000422</td>\n",
       "      <td>7.00</td>\n",
       "      <td>7.15</td>\n",
       "      <td>6.73</td>\n",
       "      <td>6.75</td>\n",
       "      <td>6.90</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.021739</td>\n",
       "      <td>0.028556</td>\n",
       "      <td>4040880</td>\n",
       "      <td>28086200</td>\n",
       "      <td>1664110000</td>\n",
       "      <td>955162000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2649</th>\n",
       "      <td>2005-02-02</td>\n",
       "      <td>'000422</td>\n",
       "      <td>6.42</td>\n",
       "      <td>6.99</td>\n",
       "      <td>6.42</td>\n",
       "      <td>6.90</td>\n",
       "      <td>6.42</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.074766</td>\n",
       "      <td>0.032928</td>\n",
       "      <td>4659550</td>\n",
       "      <td>31345900</td>\n",
       "      <td>1701090000</td>\n",
       "      <td>976388000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2650</th>\n",
       "      <td>2005-02-01</td>\n",
       "      <td>'000422</td>\n",
       "      <td>6.78</td>\n",
       "      <td>6.89</td>\n",
       "      <td>6.30</td>\n",
       "      <td>6.42</td>\n",
       "      <td>6.81</td>\n",
       "      <td>-0.39</td>\n",
       "      <td>-0.057269</td>\n",
       "      <td>0.027348</td>\n",
       "      <td>3869880</td>\n",
       "      <td>25333700</td>\n",
       "      <td>1582760000</td>\n",
       "      <td>908465000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2651</th>\n",
       "      <td>2005-01-31</td>\n",
       "      <td>'000422</td>\n",
       "      <td>6.78</td>\n",
       "      <td>6.87</td>\n",
       "      <td>6.70</td>\n",
       "      <td>6.81</td>\n",
       "      <td>6.94</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>-0.018732</td>\n",
       "      <td>0.018862</td>\n",
       "      <td>2669130</td>\n",
       "      <td>18135800</td>\n",
       "      <td>1678910000</td>\n",
       "      <td>963652000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2567 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date     Code  Open  High   Low  Close  Pre_Close  Change  \\\n",
       "0    2015-12-31  '000422  7.93  7.95  7.76   7.77       7.93   -0.16   \n",
       "1    2015-12-30  '000422  7.86  7.93  7.75   7.93       7.84    0.09   \n",
       "2    2015-12-29  '000422  7.72  7.85  7.69   7.84       7.71    0.13   \n",
       "3    2015-12-28  '000422  8.03  8.08  7.70   7.71       8.03   -0.32   \n",
       "4    2015-12-25  '000422  8.03  8.05  7.93   8.03       7.99    0.04   \n",
       "...         ...      ...   ...   ...   ...    ...        ...     ...   \n",
       "2647 2005-02-04  '000422  6.75  7.05  6.71   7.00       6.75    0.25   \n",
       "2648 2005-02-03  '000422  7.00  7.15  6.73   6.75       6.90   -0.15   \n",
       "2649 2005-02-02  '000422  6.42  6.99  6.42   6.90       6.42    0.48   \n",
       "2650 2005-02-01  '000422  6.78  6.89  6.30   6.42       6.81   -0.39   \n",
       "2651 2005-01-31  '000422  6.78  6.87  6.70   6.81       6.94   -0.13   \n",
       "\n",
       "       Chg_Pct  Turnover    Volume     Amount       Total      Circle  \n",
       "0    -0.020177  0.015498  13915200  109318000  6976420000  6976420000  \n",
       "1     0.011480  0.018662  16755900  131567000  7120080000  7120080000  \n",
       "2     0.016861  0.015886  14263800  110789000  7039280000  7039280000  \n",
       "3    -0.039851  0.030821  27672800  218869000  6922550000  6922550000  \n",
       "4     0.005006  0.021132  18974000  151673000  7209870000  7209870000  \n",
       "...        ...       ...       ...        ...         ...         ...  \n",
       "2647  0.037037  0.017178   2430800   16818500  1725750000   990538000  \n",
       "2648 -0.021739  0.028556   4040880   28086200  1664110000   955162000  \n",
       "2649  0.074766  0.032928   4659550   31345900  1701090000   976388000  \n",
       "2650 -0.057269  0.027348   3869880   25333700  1582760000   908465000  \n",
       "2651 -0.018732  0.018862   2669130   18135800  1678910000   963652000  \n",
       "\n",
       "[2567 rows x 14 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 筛选数据: 筛选出成交量 (Volume) 不为 0.0 的数据。\n",
    "# ##################################################\n",
    "StkPDF = StkPDF[StkPDF[\"Volume\"] != 0.0]\n",
    "\n",
    "# ##################################################\n",
    "StkPDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "404a85e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Code</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Pre_Close</th>\n",
       "      <th>Change</th>\n",
       "      <th>Chg_Pct</th>\n",
       "      <th>Turnover</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Total</th>\n",
       "      <th>Circle</th>\n",
       "      <th>Row_Num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-12-31</td>\n",
       "      <td>'000422</td>\n",
       "      <td>7.93</td>\n",
       "      <td>7.95</td>\n",
       "      <td>7.76</td>\n",
       "      <td>7.77</td>\n",
       "      <td>7.93</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>-0.020177</td>\n",
       "      <td>0.015498</td>\n",
       "      <td>13915200</td>\n",
       "      <td>109318000</td>\n",
       "      <td>6976420000</td>\n",
       "      <td>6976420000</td>\n",
       "      <td>2567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-12-30</td>\n",
       "      <td>'000422</td>\n",
       "      <td>7.86</td>\n",
       "      <td>7.93</td>\n",
       "      <td>7.75</td>\n",
       "      <td>7.93</td>\n",
       "      <td>7.84</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.011480</td>\n",
       "      <td>0.018662</td>\n",
       "      <td>16755900</td>\n",
       "      <td>131567000</td>\n",
       "      <td>7120080000</td>\n",
       "      <td>7120080000</td>\n",
       "      <td>2566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-12-29</td>\n",
       "      <td>'000422</td>\n",
       "      <td>7.72</td>\n",
       "      <td>7.85</td>\n",
       "      <td>7.69</td>\n",
       "      <td>7.84</td>\n",
       "      <td>7.71</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.016861</td>\n",
       "      <td>0.015886</td>\n",
       "      <td>14263800</td>\n",
       "      <td>110789000</td>\n",
       "      <td>7039280000</td>\n",
       "      <td>7039280000</td>\n",
       "      <td>2565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-12-28</td>\n",
       "      <td>'000422</td>\n",
       "      <td>8.03</td>\n",
       "      <td>8.08</td>\n",
       "      <td>7.70</td>\n",
       "      <td>7.71</td>\n",
       "      <td>8.03</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>-0.039851</td>\n",
       "      <td>0.030821</td>\n",
       "      <td>27672800</td>\n",
       "      <td>218869000</td>\n",
       "      <td>6922550000</td>\n",
       "      <td>6922550000</td>\n",
       "      <td>2564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-12-25</td>\n",
       "      <td>'000422</td>\n",
       "      <td>8.03</td>\n",
       "      <td>8.05</td>\n",
       "      <td>7.93</td>\n",
       "      <td>8.03</td>\n",
       "      <td>7.99</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.005006</td>\n",
       "      <td>0.021132</td>\n",
       "      <td>18974000</td>\n",
       "      <td>151673000</td>\n",
       "      <td>7209870000</td>\n",
       "      <td>7209870000</td>\n",
       "      <td>2563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2647</th>\n",
       "      <td>2005-02-04</td>\n",
       "      <td>'000422</td>\n",
       "      <td>6.75</td>\n",
       "      <td>7.05</td>\n",
       "      <td>6.71</td>\n",
       "      <td>7.00</td>\n",
       "      <td>6.75</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.037037</td>\n",
       "      <td>0.017178</td>\n",
       "      <td>2430800</td>\n",
       "      <td>16818500</td>\n",
       "      <td>1725750000</td>\n",
       "      <td>990538000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2648</th>\n",
       "      <td>2005-02-03</td>\n",
       "      <td>'000422</td>\n",
       "      <td>7.00</td>\n",
       "      <td>7.15</td>\n",
       "      <td>6.73</td>\n",
       "      <td>6.75</td>\n",
       "      <td>6.90</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.021739</td>\n",
       "      <td>0.028556</td>\n",
       "      <td>4040880</td>\n",
       "      <td>28086200</td>\n",
       "      <td>1664110000</td>\n",
       "      <td>955162000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2649</th>\n",
       "      <td>2005-02-02</td>\n",
       "      <td>'000422</td>\n",
       "      <td>6.42</td>\n",
       "      <td>6.99</td>\n",
       "      <td>6.42</td>\n",
       "      <td>6.90</td>\n",
       "      <td>6.42</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.074766</td>\n",
       "      <td>0.032928</td>\n",
       "      <td>4659550</td>\n",
       "      <td>31345900</td>\n",
       "      <td>1701090000</td>\n",
       "      <td>976388000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2650</th>\n",
       "      <td>2005-02-01</td>\n",
       "      <td>'000422</td>\n",
       "      <td>6.78</td>\n",
       "      <td>6.89</td>\n",
       "      <td>6.30</td>\n",
       "      <td>6.42</td>\n",
       "      <td>6.81</td>\n",
       "      <td>-0.39</td>\n",
       "      <td>-0.057269</td>\n",
       "      <td>0.027348</td>\n",
       "      <td>3869880</td>\n",
       "      <td>25333700</td>\n",
       "      <td>1582760000</td>\n",
       "      <td>908465000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2651</th>\n",
       "      <td>2005-01-31</td>\n",
       "      <td>'000422</td>\n",
       "      <td>6.78</td>\n",
       "      <td>6.87</td>\n",
       "      <td>6.70</td>\n",
       "      <td>6.81</td>\n",
       "      <td>6.94</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>-0.018732</td>\n",
       "      <td>0.018862</td>\n",
       "      <td>2669130</td>\n",
       "      <td>18135800</td>\n",
       "      <td>1678910000</td>\n",
       "      <td>963652000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2567 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date     Code  Open  High   Low  Close  Pre_Close  Change  \\\n",
       "0    2015-12-31  '000422  7.93  7.95  7.76   7.77       7.93   -0.16   \n",
       "1    2015-12-30  '000422  7.86  7.93  7.75   7.93       7.84    0.09   \n",
       "2    2015-12-29  '000422  7.72  7.85  7.69   7.84       7.71    0.13   \n",
       "3    2015-12-28  '000422  8.03  8.08  7.70   7.71       8.03   -0.32   \n",
       "4    2015-12-25  '000422  8.03  8.05  7.93   8.03       7.99    0.04   \n",
       "...         ...      ...   ...   ...   ...    ...        ...     ...   \n",
       "2647 2005-02-04  '000422  6.75  7.05  6.71   7.00       6.75    0.25   \n",
       "2648 2005-02-03  '000422  7.00  7.15  6.73   6.75       6.90   -0.15   \n",
       "2649 2005-02-02  '000422  6.42  6.99  6.42   6.90       6.42    0.48   \n",
       "2650 2005-02-01  '000422  6.78  6.89  6.30   6.42       6.81   -0.39   \n",
       "2651 2005-01-31  '000422  6.78  6.87  6.70   6.81       6.94   -0.13   \n",
       "\n",
       "       Chg_Pct  Turnover    Volume     Amount       Total      Circle  Row_Num  \n",
       "0    -0.020177  0.015498  13915200  109318000  6976420000  6976420000     2567  \n",
       "1     0.011480  0.018662  16755900  131567000  7120080000  7120080000     2566  \n",
       "2     0.016861  0.015886  14263800  110789000  7039280000  7039280000     2565  \n",
       "3    -0.039851  0.030821  27672800  218869000  6922550000  6922550000     2564  \n",
       "4     0.005006  0.021132  18974000  151673000  7209870000  7209870000     2563  \n",
       "...        ...       ...       ...        ...         ...         ...      ...  \n",
       "2647  0.037037  0.017178   2430800   16818500  1725750000   990538000        5  \n",
       "2648 -0.021739  0.028556   4040880   28086200  1664110000   955162000        4  \n",
       "2649  0.074766  0.032928   4659550   31345900  1701090000   976388000        3  \n",
       "2650 -0.057269  0.027348   3869880   25333700  1582760000   908465000        2  \n",
       "2651 -0.018732  0.018862   2669130   18135800  1678910000   963652000        1  \n",
       "\n",
       "[2567 rows x 15 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算数据: 分配行号。\n",
    "# ##################################################\n",
    "SourceDataPDF = StkPDF.copy()\n",
    "\n",
    "# 排序计算: rank 函数达到 SQL 中类似 ROW_NUMBER 的功能。\n",
    "# --------------------------------------------------\n",
    "TEMPPDF = SourceDataPDF\n",
    "# ..................................................\n",
    "Sorted = TEMPPDF.sort_values(\"Date\", ascending=True)\n",
    "# ..................................................\n",
    "TEMPPDF[\"Row_Num\"] = Sorted[\"Date\"].rank(ascending=True, method='first')\n",
    "# ..................................................\n",
    "TEMPPDF[\"Row_Num\"] = TEMPPDF[\"Row_Num\"].astype(\"int64\")\n",
    "# ..................................................\n",
    "StkNumberedPDF = TEMPPDF\n",
    "\n",
    "# ##################################################\n",
    "StkNumberedPDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5cfad848",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Row_Num</th>\n",
       "      <th>Date</th>\n",
       "      <th>Close</th>\n",
       "      <th>RSI6</th>\n",
       "      <th>RSI12</th>\n",
       "      <th>RSI24</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2651</th>\n",
       "      <td>1</td>\n",
       "      <td>2005-01-31</td>\n",
       "      <td>6.81</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2650</th>\n",
       "      <td>2</td>\n",
       "      <td>2005-02-01</td>\n",
       "      <td>6.42</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2649</th>\n",
       "      <td>3</td>\n",
       "      <td>2005-02-02</td>\n",
       "      <td>6.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2648</th>\n",
       "      <td>4</td>\n",
       "      <td>2005-02-03</td>\n",
       "      <td>6.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2647</th>\n",
       "      <td>5</td>\n",
       "      <td>2005-02-04</td>\n",
       "      <td>7.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2563</td>\n",
       "      <td>2015-12-25</td>\n",
       "      <td>8.03</td>\n",
       "      <td>78.431373</td>\n",
       "      <td>58.333333</td>\n",
       "      <td>55.942029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2564</td>\n",
       "      <td>2015-12-28</td>\n",
       "      <td>7.71</td>\n",
       "      <td>55.555556</td>\n",
       "      <td>51.470588</td>\n",
       "      <td>51.604278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2565</td>\n",
       "      <td>2015-12-29</td>\n",
       "      <td>7.84</td>\n",
       "      <td>50.769231</td>\n",
       "      <td>62.406015</td>\n",
       "      <td>52.741514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2566</td>\n",
       "      <td>2015-12-30</td>\n",
       "      <td>7.93</td>\n",
       "      <td>52.941176</td>\n",
       "      <td>61.832061</td>\n",
       "      <td>54.241645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2567</td>\n",
       "      <td>2015-12-31</td>\n",
       "      <td>7.77</td>\n",
       "      <td>40.740741</td>\n",
       "      <td>57.857143</td>\n",
       "      <td>59.269663</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2567 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Row_Num       Date  Close       RSI6      RSI12      RSI24\n",
       "2651        1 2005-01-31   6.81        NaN        NaN        NaN\n",
       "2650        2 2005-02-01   6.42        NaN        NaN        NaN\n",
       "2649        3 2005-02-02   6.90        NaN        NaN        NaN\n",
       "2648        4 2005-02-03   6.75        NaN        NaN        NaN\n",
       "2647        5 2005-02-04   7.00        NaN        NaN        NaN\n",
       "...       ...        ...    ...        ...        ...        ...\n",
       "4        2563 2015-12-25   8.03  78.431373  58.333333  55.942029\n",
       "3        2564 2015-12-28   7.71  55.555556  51.470588  51.604278\n",
       "2        2565 2015-12-29   7.84  50.769231  62.406015  52.741514\n",
       "1        2566 2015-12-30   7.93  52.941176  61.832061  54.241645\n",
       "0        2567 2015-12-31   7.77  40.740741  57.857143  59.269663\n",
       "\n",
       "[2567 rows x 6 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算数据: 金融指标(RSI)。\n",
    "# ##################################################\n",
    "SourceDataPDF = StkNumberedPDF.sort_values(\"Date\", ascending=True)\n",
    "\n",
    "# 计算 RSI。\n",
    "# --------------------------------------------------\n",
    "TEMPPDF = SourceDataPDF\n",
    "# ..................................................\n",
    "# DataFrame 中的 apply:\n",
    "# -> 当 axis=1 时，对每行执行指定函数。\n",
    "# -> 当 axis=0 时，对每列执行指定函数。\n",
    "TEMPPDF[\"RSI6\"] = TEMPPDF.apply(lambda X: MapFunc_FinInd_RSI(Index=X[\"Row_Num\"], Period=6, Change=X[\"Change\"]), axis=1)\n",
    "TEMPPDF[\"RSI12\"] = TEMPPDF.apply(lambda X: MapFunc_FinInd_RSI(Index=X[\"Row_Num\"], Period=12, Change=X[\"Change\"]), axis=1)\n",
    "TEMPPDF[\"RSI24\"] = TEMPPDF.apply(lambda X: MapFunc_FinInd_RSI(Index=X[\"Row_Num\"], Period=24, Change=X[\"Change\"]), axis=1)\n",
    "# ..................................................\n",
    "StkIndicatorPDF = TEMPPDF\n",
    "\n",
    "# ##################################################\n",
    "StkIndicatorPDF[[\"Row_Num\", \"Date\", \"Close\", \"RSI6\", \"RSI12\", \"RSI24\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c5cc6264",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1536x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Matplotlib 绘制数据。\n",
    "# ##################################################\n",
    "DrawPDF = StkIndicatorPDF\n",
    "\n",
    "# 筛选数据: 放大局部数据。\n",
    "# --------------------------------------------------\n",
    "#DrawPDF = DrawPDF[(200 <= DrawPDF[\"Row_Num\"]) & (DrawPDF[\"Row_Num\"] <= 1500)]\n",
    "DrawPDF = DrawPDF[(500 <= DrawPDF[\"Row_Num\"]) & (DrawPDF[\"Row_Num\"] <= 750)]\n",
    "\n",
    "# 画布配置。\n",
    "# --------------------------------------------------\n",
    "fig = plt.figure(figsize=(16, 9), dpi=96)\n",
    "Grid = plt.GridSpec(4, 1, figure=fig)\n",
    "\n",
    "# K 线数据: 红绿 K 线的 Bottom、Height。\n",
    "# --------------------------------------------------\n",
    "KLine_Red = DrawPDF[DrawPDF[\"Change\"] >= 0.0]\n",
    "KLine_Red_Bottom = KLine_Red[\"Open\"]\n",
    "KLine_Red_Height = KLine_Red[\"Close\"] - KLine_Red[\"Open\"]\n",
    "# ..................................................\n",
    "KLine_Gre = DrawPDF[DrawPDF[\"Change\"] <  0.0]\n",
    "KLine_Gre_Bottom = KLine_Gre[\"Close\"]\n",
    "KLine_Gre_Height = KLine_Gre[\"Open\"] - KLine_Gre[\"Close\"]\n",
    "\n",
    "# 绘制数据: 绘制红绿 K 线。\n",
    "# --------------------------------------------------\n",
    "ax1 = plt.subplot(Grid[0:3])\n",
    "# ..................................................\n",
    "ax1.bar(x=KLine_Red[\"Row_Num\"], height=KLine_Red_Height, width=0.6, bottom=KLine_Red_Bottom, color=\"#FF4500\")\n",
    "ax1.bar(x=KLine_Gre[\"Row_Num\"], height=KLine_Gre_Height, width=0.6, bottom=KLine_Gre_Bottom, color=\"#228B22\")\n",
    "# ..................................................\n",
    "ax1.vlines(x=KLine_Red[\"Row_Num\"], ymin=KLine_Red[\"Low\"], ymax=KLine_Red[\"High\"], colors=\"#FF4500\", linewidth=1)\n",
    "ax1.vlines(x=KLine_Gre[\"Row_Num\"], ymin=KLine_Gre[\"Low\"], ymax=KLine_Gre[\"High\"], colors=\"#228B22\", linewidth=1)\n",
    "\n",
    "# 绘制数据: 绘制 RSI 指标。\n",
    "# --------------------------------------------------\n",
    "ax2 = plt.subplot(Grid[3:4])\n",
    "# ..................................................\n",
    "ax2.plot(DrawPDF[\"Row_Num\"], DrawPDF[\"RSI6\"], color=\"#000000\", label=\"RSI6\")\n",
    "ax2.plot(DrawPDF[\"Row_Num\"], DrawPDF[\"RSI12\"], color=\"#FFA500\", label=\"RSI12\")\n",
    "ax2.plot(DrawPDF[\"Row_Num\"], DrawPDF[\"RSI24\"], color=\"#FF00FF\", label=\"RSI24\")\n",
    "# ..................................................\n",
    "ax2.hlines(y=80, xmin=DrawPDF[\"Row_Num\"].min(), xmax=DrawPDF[\"Row_Num\"].max(), linestyle=\"dashed\", color=\"#708090\")\n",
    "ax2.hlines(y=50, xmin=DrawPDF[\"Row_Num\"].min(), xmax=DrawPDF[\"Row_Num\"].max(), linestyle=\"dashed\", color=\"#708090\")\n",
    "ax2.hlines(y=20, xmin=DrawPDF[\"Row_Num\"].min(), xmax=DrawPDF[\"Row_Num\"].max(), linestyle=\"dashed\", color=\"#708090\")\n",
    "\n",
    "# ##################################################\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "595e5dad",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
